MC vs MI Wave 1 analyses

1 intro

## libraries ----

library(here)
library(data.table)
source(here("src", "stroop-rsa-pc.R"))
library(knitr)
library(dplyr)
library(tidyr)
library(ggplot2)
library(mfutils)
library(ggseg)
library(ggsegExtra)
library(ggsegSchaefer)
library(ggsegGlasser)

## settings, params ----

opts_chunk$set(echo = TRUE, out.width = "100%")
theme_set(theme_bw(base_size = 10))

if (exists("params")) {
  ttype_subset <- params$ttype_subset
  prewh <- params$prewh
  measure <- params$measure
  roiset <- params$roiset
} else {
  prewh <- "none"
  measure <- "crcor"
  roiset <- "glasser2016_parcel"
}

theme_surface <- list(
  theme(
    axis.text = element_blank(), panel.grid = element_blank(), panel.border = element_blank(),
    axis.ticks = element_blank(), legend.position = c(0.5, 0.5), legend.title = element_text(size = 5), 
    legend.background = element_blank(), legend.text = element_text(size = 5), legend.direction = "horizontal",
    legend.key.height = unit(1/5, "cm"), legend.key.width = unit(1/4, "cm")
  )
)


## constants ----

wd <- here()
subjlists <- c("mc1", "mi1")
seswaves <- c("baseline_wave1", "proactive_wave1")
sessions <- c("baseline", "proactive")
glmname <- "lsall_1rpm"
statistics <- c("m", "t_stat")
titles <- c("mean coefficient", "t statistic")
ttype_subsets <- c("pc50", "bias")

## data ----

## read regression weights:

dat <- lapply(
  ttype_subsets,
  function(ttype_subset) {
    fname <- construct_filename_weights(
        measure = measure, subjlist = subjlists, glmname = glmname, 
        ttype_subset = ttype_subset, roiset = roiset, prewh = prewh, suffix = paste0("__seswave-", seswaves)
        )
    d <- rbindlist(lapply(fname, fread))
    d$ttype_subset <- ttype_subset
    d
  }
)
dat <- rbindlist(dat)


## get ggseg atlas and format data to match:

dat <- dat %>% rename(region = roi)

if (roiset == "schaefer2018_17_200_parcel") {
  
  atlas <- schaefer17_200
  k <- schaefer2018_17_200_fsaverage5$key %>% select(region = parcel, network)
  
  rois <- data.frame(
    region = c(
    "17Networks_LH_ContA_PFClv_1",
    "17Networks_LH_ContA_PFCl_1",
    "17Networks_LH_ContA_PFCl_2",
    "17Networks_LH_ContA_PFCl_3",
    "17Networks_RH_ContA_PFCl_1",
    "17Networks_RH_ContA_PFCl_2",
    "17Networks_LH_SalVentAttnB_PFCmp_1",
    "17Networks_RH_SalVentAttnB_PFCmp_1",
    "17Networks_LH_SalVentAttnA_FrMed_1",
    "17Networks_RH_SalVentAttnA_FrMed_1"
    ),
    location = c(rep("lateral", 6), rep("medial", 4))
  )

} else if (roiset == "glasser2016_parcel") {
  
  atlas <- glasser
  k <- glasser2016_fsaverage5$key %>% select(region = parcel, network)
  
  dat <- dat %>% filter(!region %in% "L_10pp")  ## not in glasser ggseg atlas?
  ## create region column in ggseg atlas that matches data:
  hemi <- ifelse(atlas$data$hemi == "left", "L_", "R_")
  atlas$data$region <- ifelse(is.na(atlas$data$region), NA, paste0(hemi, atlas$data$region))
  
  rois <- data.frame(
    region = combo_paste(c("L_", "R_"), c("SCEF", "8BM",  "p32pr", "a32pr", "p9-46v", "i6-8", "8Av", "8C")),
    location = c(rep("medial", 8), rep("lateral", 8))
  )
  
}

## add network information:
atlas$data <- left_join(atlas$data, k, by = "region")
dat <- left_join(dat, k, by = "region")


## calculate ----

## add subject set information:

subjs <- fread(here("out", "subjlist_mcmi.txt"))[[1]]
dat <- dat[subject %in% subjs]


dat_sum <- dat %>% 
  group_by(term, ttype_subset, session, region) %>%
  summarize(
    m = mean(b),
    t_stat = t.test(b)$statistic, 
    p = t.test(b, alternative = "greater")$p.value,
    .groups = "drop_last"
    ) %>%
  mutate(p_fdr = p.adjust(p, "fdr"))


dat_diff <- dat %>%
  pivot_wider(id_cols = c("subject", "term", "region", "ttype_subset"), names_from = "session", values_from = "b") %>%
  mutate(b = proactive - baseline) %>%
  group_by(term, ttype_subset, region) %>%
  summarize(
    m = mean(b),
    t_stat = t.test(b)$statistic,
    p = t.test(b)$p.value,
    .groups = "drop_last"
    ) %>%
  mutate(p_fdr = p.adjust(p, "fdr"), session = "pro - bas")


dat_sum <- filter(rbind(dat_sum, dat_diff), term %in% models[[measure]])
dat_sum <- dat_sum %>% group_by(term, ttype_subset, session)  ## for appropriate binding/plotting w/ ggseg


n_subjs <- length(unique(dat$subject))

This file displays group-level RSA model coefficients (means, test statistics).

Analysis parameters:

• Item type:
• Prewhitening type: none
• Similarity measure: crcor
• ROI set: glasser2016_parcel

Subject samples:

Analyses are conducted with one set of subjects:

• the MCMIset: N = 63, unrelated, with baseline and proactive data

2 Brains

2.1 Unthresholded

2.1.1 Common scale

for (ttype_subset_i in seq_along(ttype_subsets)) {
  for (stat_i in seq_along(statistics)) {
    
    p <- dat_sum %>%
      group_by(term, ttype_subset, session) %>%
      filter(ttype_subset == ttype_subsets[ttype_subset_i]) %>%
      ggplot() +
      geom_brain(
        aes_string(fill = statistics[stat_i]), atlas = atlas, position = position_brain(side ~ hemi)
        ) +
      scale_fill_viridis_c(
        option = "magma", na.value = "grey",
        breaks = scales::extended_breaks(4)
        ) +
      facet_grid(rows = vars(term), cols = vars(session)) +
      theme_surface +
      labs(title = paste0(titles[stat_i], " ", ttype_subsets[ttype_subset_i]), fill = NULL)
    
    print(p)
  
  }
}

2.1.2 Separate scales by model

for (model_i in seq_along(models[[measure]])) {

  model <- models[[measure]][model_i]
  
  for (stat_i in seq_along(statistics)) {

    p <- dat_sum %>%
      filter(term %in% model) %>%
      ggplot() +
      geom_brain(
        aes_string(fill = statistics[stat_i]),
        atlas = atlas, position = position_brain(side ~ hemi)
        ) +
      scale_fill_viridis_c(
        option = "magma", na.value = "grey",
        breaks = scales::extended_breaks(4)
        ) +
      facet_grid(rows = vars(ttype_subset), cols = vars(session)) +
      theme_surface +
      theme(legend.position = c(2/3, 0.5)) +
      labs(title = paste0(titles[stat_i], ": ", model), fill = NULL)

    print(p)

  }

}

2.2 Thresholded

Three consecutive thresholds are used, each more liberal than the previous:

• p_FDR < 0.05: parcels with mean model fit significantly greater than 0 following FDR correction over all parcels (separately per model and trialtype set)
• p_uncorr < 0.05: parcels with mean model fit significantly greater than 0 (uncorrected)
• t_stat > 0: parcels with mean model fit numerically greater than zero

2.2.1 Common scale

dat_sum$is_in_thresh_fdr <- dat_sum$p_fdr < 0.05
dat_sum$is_in_thresh_p <- dat_sum$p < 0.05
dat_sum$is_in_thresh_sign <- dat_sum$t_stat > 0

thresholds <- c("p_fdr < 0.05", "p_uncorr < 0.05", "t_stat > 0")
thresholds_nms <- c("is_in_thresh_fdr", "is_in_thresh_p", "is_in_thresh_sign")

for (ttype_subset_i in seq_along(ttype_subsets)) {

  for (thresh_i in seq_along(thresholds)) {
  
    threshold_nm <- thresholds_nms[thresh_i]
  
    for (stat_i in seq_along(statistics)) {
  
      dat_sum$thresh <- as.numeric(ifelse(dat_sum[[threshold_nm]], dat_sum[[statistics[stat_i]]], NA))
  
      p <- dat_sum %>%
        filter(ttype_subset == ttype_subsets[ttype_subset_i]) %>%
        ggplot() +
        geom_brain(
          aes(fill = thresh),
          atlas = atlas, position = position_brain(side ~ hemi)
          ) +
        scale_fill_viridis_c(
          option = "magma", na.value = "grey",
          breaks = scales::extended_breaks(4)
          ) +
        facet_grid(rows = vars(term), cols = vars(session)) +
        theme_surface +
        labs(
          title = paste0(
            titles[stat_i], " ", ttype_subsets[ttype_subset_i], ": thresholded at ", thresholds[thresh_i]
            ), fill = NULL
          )
  
      print(p)
      
    }
  
  }

}

3 Tables

3.1 Baseline+Proactive conjunction analysis

Parcels that are FDR-significant within both baseline and proactive sessions. Separately for bias and pc50 items.

3.1.1 FDR corrected whole-cortex

for (ttypesub in ttype_subsets) {
  
  sig_both <- dat_sum %>% 
    filter(p_fdr < 0.05, ttype_subset == ttypesub, session %in% c("baseline", "proactive")) %>% 
    group_by(term, region) %>% 
    summarize(is_sig_both = n() == 2) %>%
    filter(is_sig_both)
  
  d <- enlist(c("target", "distractor", "incongruency"))
  for (model in names(d)) {
    
    regions <- sig_both$region[sig_both$term == model & sig_both$term == model]
    
    d[[model]] <- dat_sum %>%
      filter(ttype_subset == ttypesub, term == model, region %in% regions) %>%
      ungroup %>%
      select(region, session, m, t_stat, p) %>%
      arrange(region, session)
  
  }
  
  print(lapply(names(d), function(x) kable(d[[x]], caption = paste0(ttypesub, " ", x))))
  
}

## `summarise()` has grouped output by 'term'. You can override using the `.groups` argument.

[[1]]

pc50 target

region	session	m	t_stat	p
R_3b	baseline	0.0136747	4.2950622	0.0000313
R_3b	pro - bas	-0.0013983	-0.2850443	0.7765603
R_3b	proactive	0.0122765	3.4623801	0.0004879
R_6ma	baseline	0.0148989	3.6927110	0.0002354
R_6ma	pro - bas	-0.0010641	-0.2084019	0.8355983
R_6ma	proactive	0.0138348	3.4051636	0.0005824
R_A5	baseline	0.0194050	4.0869374	0.0000639
R_A5	pro - bas	-0.0070905	-1.2115724	0.2302736
R_A5	proactive	0.0123146	3.3820439	0.0006253

[[2]]

pc50 distractor

region	session	m	t_stat	p
L_4	baseline	0.0088508	4.2325022	0.0000389
L_4	pro - bas	0.0039386	1.0222881	0.3106170
L_4	proactive	0.0127894	4.1521684	0.0000512
R_4	baseline	0.0079654	3.4089821	0.0005756
R_4	pro - bas	0.0018181	0.4886019	0.6268474
R_4	proactive	0.0097836	3.7281412	0.0002099

[[3]]

pc50 incongruency

region	session	m	t_stat	p
L_1	baseline	0.0250155	3.2775181	0.0008593
L_1	pro - bas	-0.0019012	-0.1905058	0.8495352
L_1	proactive	0.0231143	3.0179718	0.0018447
L_31a	baseline	0.0263768	2.0181354	0.0239547
L_31a	pro - bas	0.0246165	1.3497223	0.1820127
L_31a	proactive	0.0509932	3.5906716	0.0003261
L_31pv	baseline	0.0403942	3.6714483	0.0002520
L_31pv	pro - bas	-0.0127901	-0.7825944	0.4368443
L_31pv	proactive	0.0276041	2.7280038	0.0041392
L_3a	baseline	0.0252639	3.1001865	0.0014540
L_3a	pro - bas	0.0045528	0.3546220	0.7240762
L_3a	proactive	0.0298167	3.4889843	0.0004491
L_4	baseline	0.0327898	4.1661319	0.0000488
L_4	pro - bas	0.0104880	0.9147621	0.3638606
L_4	proactive	0.0432778	5.5986734	0.0000003
L_43	baseline	0.0337807	2.7859792	0.0035356
L_43	pro - bas	-0.0075529	-0.4890418	0.6265377
L_43	proactive	0.0262278	2.8977921	0.0025942
L_44	baseline	0.0396427	3.5990727	0.0003175
L_44	pro - bas	-0.0035841	-0.2367109	0.8136614
L_44	proactive	0.0360586	3.4970868	0.0004379
L_45	baseline	0.0426258	5.1225961	0.0000016
L_45	pro - bas	-0.0165040	-1.3088533	0.1954148
L_45	proactive	0.0261218	2.9124640	0.0024895
L_46	baseline	0.0246938	2.7066229	0.0043846
L_46	pro - bas	0.0019397	0.1307783	0.8963741
L_46	proactive	0.0266335	2.3416812	0.0112126
L_55b	baseline	0.0307700	2.9457176	0.0022666
L_55b	pro - bas	-0.0027459	-0.2091955	0.8349815
L_55b	proactive	0.0280240	3.0824748	0.0015310
L_6a	baseline	0.0520084	5.3578070	0.0000007
L_6a	pro - bas	-0.0133346	-1.0345753	0.3048859
L_6a	proactive	0.0386738	3.8210171	0.0001551
L_6ma	baseline	0.0342609	3.2074755	0.0010598
L_6ma	pro - bas	0.0043704	0.3030088	0.7628975
L_6ma	proactive	0.0386313	4.1590328	0.0000500
L_6mp	baseline	0.0284588	3.9307709	0.0001080
L_6mp	pro - bas	-0.0091140	-0.7734253	0.4422097
L_6mp	proactive	0.0193449	2.1664022	0.0170665
L_6r	baseline	0.0542686	5.2269459	0.0000011
L_6r	pro - bas	-0.0138833	-1.0842855	0.2824367
L_6r	proactive	0.0403853	4.9424939	0.0000031
L_6v	baseline	0.0493127	4.4708149	0.0000169
L_6v	pro - bas	-0.0023553	-0.1868891	0.8523578
L_6v	proactive	0.0469575	5.0701595	0.0000019
L_7m	baseline	0.0357428	3.4012227	0.0005895
L_7m	pro - bas	-0.0007814	-0.0512561	0.9592862
L_7m	proactive	0.0349614	3.0353259	0.0017548
L_7Pm	baseline	0.0406123	2.9788018	0.0020634
L_7Pm	pro - bas	0.0092868	0.5424678	0.5894409
L_7Pm	proactive	0.0498991	4.2204511	0.0000405
L_8Ad	baseline	0.0357119	4.0761918	0.0000663
L_8Ad	pro - bas	-0.0092451	-0.7578026	0.4514399
L_8Ad	proactive	0.0264668	3.4193251	0.0005575
L_8BM	baseline	0.0332537	3.0891690	0.0015014
L_8BM	pro - bas	0.0093455	0.6714989	0.5043971
L_8BM	proactive	0.0425992	4.5949320	0.0000109
L_8C	baseline	0.0675154	5.6651835	0.0000002
L_8C	pro - bas	-0.0120092	-0.8188014	0.4160359
L_8C	proactive	0.0555062	6.2544948	0.0000000
L_9-46d	baseline	0.0221135	2.6532507	0.0050565
L_9-46d	pro - bas	0.0004054	0.0355322	0.9717695
L_9-46d	proactive	0.0225189	2.2732554	0.0132431
L_9a	baseline	0.0348476	3.4304778	0.0005386
L_9a	pro - bas	-0.0146061	-1.1011688	0.2750799
L_9a	proactive	0.0202415	2.4612645	0.0083208
L_9p	baseline	0.0329332	3.5487265	0.0003723
L_9p	pro - bas	-0.0077115	-0.5790275	0.5646685
L_9p	proactive	0.0252217	2.5603641	0.0064531
L_AIP	baseline	0.0795479	7.4639684	0.0000000
L_AIP	pro - bas	-0.0433342	-3.1774166	0.0023175
L_AIP	proactive	0.0362137	3.7355071	0.0002050
L_d32	baseline	0.0337588	3.6433829	0.0002757
L_d32	pro - bas	0.0027145	0.1925211	0.8479632
L_d32	proactive	0.0364733	4.0245498	0.0000789
L_FEF	baseline	0.0358484	3.5799895	0.0003373
L_FEF	pro - bas	-0.0034715	-0.2522377	0.8016916
L_FEF	proactive	0.0323769	3.1894405	0.0011182
L_FOP4	baseline	0.0584673	5.6159944	0.0000002
L_FOP4	pro - bas	-0.0341188	-2.7237538	0.0083738
L_FOP4	proactive	0.0243484	2.6241083	0.0054618
L_FOP5	baseline	0.0322949	3.1041045	0.0014375
L_FOP5	pro - bas	-0.0091393	-0.6766664	0.5011345
L_FOP5	proactive	0.0231556	2.7934260	0.0034643
L_FST	baseline	0.0524240	4.5424812	0.0000131
L_FST	pro - bas	-0.0218283	-1.5351660	0.1298308
L_FST	proactive	0.0305956	3.1146965	0.0013936
L_i6-8	baseline	0.0533561	4.3524360	0.0000256
L_i6-8	pro - bas	-0.0251549	-1.4547021	0.1507961
L_i6-8	proactive	0.0282012	2.8378694	0.0030652
L_IFJa	baseline	0.0806943	6.4804739	0.0000000
L_IFJa	pro - bas	-0.0430783	-2.4116645	0.0188546
L_IFJa	proactive	0.0376161	3.2672187	0.0008863
L_IFJp	baseline	0.0544792	4.2628561	0.0000350
L_IFJp	pro - bas	-0.0155944	-0.8962073	0.3736087
L_IFJp	proactive	0.0388848	3.3563321	0.0006765
L_IFSa	baseline	0.0773138	6.0667898	0.0000000
L_IFSa	pro - bas	-0.0138961	-0.8734622	0.3857818
L_IFSa	proactive	0.0634176	5.4528686	0.0000005
L_IFSp	baseline	0.0872288	6.1192201	0.0000000
L_IFSp	pro - bas	-0.0461277	-2.4839035	0.0157115
L_IFSp	proactive	0.0411011	3.2150464	0.0010362
L_IP0	baseline	0.0464968	3.1684795	0.0011898
L_IP0	pro - bas	0.0154885	0.8557017	0.3954577
L_IP0	proactive	0.0619853	5.2456400	0.0000010
L_IP1	baseline	0.1023714	7.9336086	0.0000000
L_IP1	pro - bas	-0.0200309	-1.1819054	0.2417566
L_IP1	proactive	0.0823405	6.9514309	0.0000000
L_IP2	baseline	0.0674154	5.7595818	0.0000001
L_IP2	pro - bas	-0.0212954	-1.4066845	0.1645139
L_IP2	proactive	0.0461200	3.6039362	0.0003127
L_IPS1	baseline	0.0431928	3.7844046	0.0001749
L_IPS1	pro - bas	-0.0035816	-0.2405617	0.8106885
L_IPS1	proactive	0.0396111	3.9137654	0.0001143
L_LIPd	baseline	0.0916975	7.0928118	0.0000000
L_LIPd	pro - bas	-0.0365356	-2.2010701	0.0314632
L_LIPd	proactive	0.0551618	5.0105870	0.0000024
L_LIPv	baseline	0.0518520	4.3080600	0.0000299
L_LIPv	pro - bas	-0.0286405	-1.9044072	0.0614994
L_LIPv	proactive	0.0232114	2.2402105	0.0143355
L_MIP	baseline	0.0783998	6.6345078	0.0000000
L_MIP	pro - bas	-0.0217185	-1.5073826	0.1367914
L_MIP	proactive	0.0566814	5.3318480	0.0000007
L_p47r	baseline	0.0269712	2.6821878	0.0046814
L_p47r	pro - bas	0.0015626	0.1201300	0.9047686
L_p47r	proactive	0.0285338	2.7202648	0.0042265
L_p9-46v	baseline	0.0658649	5.1551157	0.0000014
L_p9-46v	pro - bas	-0.0134901	-0.9150329	0.3637195
L_p9-46v	proactive	0.0523747	4.7615837	0.0000060
L_PCV	baseline	0.0317700	2.9714312	0.0021071
L_PCV	pro - bas	-0.0062594	-0.4124103	0.6814613
L_PCV	proactive	0.0255106	2.6104651	0.0056615
L_PEF	baseline	0.0726216	5.5553934	0.0000003
L_PEF	pro - bas	-0.0369481	-2.1419290	0.0361352
L_PEF	proactive	0.0356735	2.5157159	0.0072418
L_PF	baseline	0.0629092	6.7901597	0.0000000
L_PF	pro - bas	-0.0253248	-1.7703841	0.0815793
L_PF	proactive	0.0375843	3.7706636	0.0001829
L_PFcm	baseline	0.0453176	3.6612469	0.0002604
L_PFcm	pro - bas	-0.0038876	-0.2084954	0.8355256
L_PFcm	proactive	0.0414299	2.9887334	0.0020057
L_PFm	baseline	0.0377244	3.7570408	0.0001911
L_PFm	pro - bas	-0.0026710	-0.1848624	0.8539404
L_PFm	proactive	0.0350533	3.4394685	0.0005238
L_PFop	baseline	0.0399872	4.0419563	0.0000744
L_PFop	pro - bas	0.0058528	0.4649410	0.6436026
L_PFop	proactive	0.0458400	5.4558858	0.0000005
L_PFt	baseline	0.0537449	4.4772111	0.0000165
L_PFt	pro - bas	-0.0171331	-1.0229484	0.3103071
L_PFt	proactive	0.0366118	3.1915988	0.0011110
L_PGp	baseline	0.0407329	3.6077834	0.0003089
L_PGp	pro - bas	-0.0140423	-0.8345765	0.4071605
L_PGp	proactive	0.0266906	2.3980846	0.0097524
L_PGs	baseline	0.0278521	2.5418416	0.0067703
L_PGs	pro - bas	0.0007523	0.0577009	0.9541724
L_PGs	proactive	0.0286044	3.0026635	0.0019274
L_PH	baseline	0.0475936	4.2958348	0.0000312
L_PH	pro - bas	-0.0192685	-1.3636218	0.1776175
L_PH	proactive	0.0283251	3.4914997	0.0004456
L_PHA3	baseline	0.0224219	2.3941125	0.0098494
L_PHA3	pro - bas	0.0051542	0.4676927	0.6416441
L_PHA3	proactive	0.0275761	3.0840586	0.0015239
L_PHT	baseline	0.0483927	5.1854731	0.0000013
L_PHT	pro - bas	-0.0122964	-0.8730645	0.3859968
L_PHT	proactive	0.0360964	3.4607528	0.0004904
L_POS1	baseline	0.0330198	2.5832233	0.0060802
L_POS1	pro - bas	-0.0067121	-0.3899887	0.6978815
L_POS1	proactive	0.0263077	2.2798181	0.0130351
L_POS2	baseline	0.0550723	5.0799535	0.0000019
L_POS2	pro - bas	-0.0162740	-1.1270345	0.2640710
L_POS2	proactive	0.0387982	3.3295183	0.0007341
L_PSL	baseline	0.0417531	4.5745974	0.0000117
L_PSL	pro - bas	-0.0021277	-0.1424264	0.8872051
L_PSL	proactive	0.0396253	3.7749706	0.0001803
L_RI	baseline	0.0392052	4.5999912	0.0000107
L_RI	pro - bas	-0.0195815	-1.6950803	0.0950775
L_RI	proactive	0.0196238	2.2463353	0.0141272
L_RSC	baseline	0.0258053	2.1931416	0.0160285
L_RSC	pro - bas	0.0006728	0.0491730	0.9609395
L_RSC	proactive	0.0264781	2.1643232	0.0171496
L_SCEF	baseline	0.0554164	6.5583199	0.0000000
L_SCEF	pro - bas	0.0113341	0.9875780	0.3271979
L_SCEF	proactive	0.0667505	8.2820443	0.0000000
L_SFL	baseline	0.0542923	5.2660745	0.0000009
L_SFL	pro - bas	-0.0182674	-1.3188054	0.1920848
L_SFL	proactive	0.0360249	3.7469911	0.0001975
L_STSda	baseline	0.0281670	2.7207133	0.0042214
L_STSda	pro - bas	-0.0075148	-0.5553736	0.5806376
L_STSda	proactive	0.0206522	2.3820578	0.0101489
L_STV	baseline	0.0322208	2.8003133	0.0033995
L_STV	pro - bas	-0.0034371	-0.2261130	0.8218573
L_STV	proactive	0.0287837	2.8913151	0.0026416
L_TPOJ1	baseline	0.0527931	4.8358571	0.0000046
L_TPOJ1	pro - bas	-0.0018332	-0.1226753	0.9027610
L_TPOJ1	proactive	0.0509599	5.0529398	0.0000021
L_TPOJ3	baseline	0.0375986	2.7971176	0.0034294
L_TPOJ3	pro - bas	-0.0118325	-0.7007359	0.4860894
L_TPOJ3	proactive	0.0257661	2.2476134	0.0140841
L_V3	baseline	0.0358501	3.9140086	0.0001142
L_V3	pro - bas	-0.0022455	-0.1813906	0.8566527
L_V3	proactive	0.0336046	4.1499906	0.0000516
L_V4	baseline	0.0280787	2.8152002	0.0032631
L_V4	pro - bas	-0.0078472	-0.5973130	0.5524737
L_V4	proactive	0.0202315	2.3245777	0.0116923
R_23c	baseline	0.0173074	2.1802555	0.0165216
R_23c	pro - bas	0.0149526	1.4993257	0.1388642
R_23c	proactive	0.0322600	4.4148567	0.0000206
R_31pd	baseline	0.0457540	3.6014629	0.0003151
R_31pd	pro - bas	-0.0198064	-1.1679375	0.2473037
R_31pd	proactive	0.0259476	2.3223216	0.0117569
R_31pv	baseline	0.0282672	2.4979196	0.0075797
R_31pv	pro - bas	-0.0010518	-0.0607643	0.9517424
R_31pv	proactive	0.0272154	2.2128773	0.0152983
R_3a	baseline	0.0246922	2.7811763	0.0035824
R_3a	pro - bas	0.0098012	0.7835488	0.4362880
R_3a	proactive	0.0344934	4.6857796	0.0000079
R_3b	baseline	0.0224102	2.9675417	0.0021306
R_3b	pro - bas	0.0000109	0.0009842	0.9992179
R_3b	proactive	0.0224211	3.1683479	0.0011902
R_4	baseline	0.0293674	4.5716356	0.0000118
R_4	pro - bas	0.0022981	0.2330836	0.8164643
R_4	proactive	0.0316655	4.4943172	0.0000156
R_43	baseline	0.0198482	1.9996649	0.0249615
R_43	pro - bas	0.0219503	1.6946413	0.0951613
R_43	proactive	0.0417985	4.1721196	0.0000478
R_44	baseline	0.0280822	2.3358055	0.0113754
R_44	pro - bas	0.0065236	0.3895087	0.6982347
R_44	proactive	0.0346058	3.3557443	0.0006777
R_45	baseline	0.0353285	3.2198493	0.0010215
R_45	pro - bas	-0.0051494	-0.3014775	0.7640592
R_45	proactive	0.0301791	2.5385184	0.0068287
R_46	baseline	0.0279360	3.5122343	0.0004176
R_46	pro - bas	-0.0061214	-0.5463945	0.5867558
R_46	proactive	0.0218146	2.7395948	0.0040114
R_6a	baseline	0.0353947	4.3378584	0.0000270
R_6a	pro - bas	-0.0086521	-0.7578622	0.4514045
R_6a	proactive	0.0267426	2.7354884	0.0040563
R_6ma	baseline	0.0430124	4.6204896	0.0000099
R_6ma	pro - bas	0.0193867	1.5580643	0.1243083
R_6ma	proactive	0.0623991	7.3603747	0.0000000
R_6mp	baseline	0.0190499	3.1160971	0.0013879
R_6mp	pro - bas	0.0059751	0.6330840	0.5290071
R_6mp	proactive	0.0250250	3.1042547	0.0014368
R_6r	baseline	0.0558549	5.3014662	0.0000008
R_6r	pro - bas	-0.0173321	-1.4088027	0.1638891
R_6r	proactive	0.0385228	4.7167279	0.0000070
R_6v	baseline	0.0433786	4.0437209	0.0000740
R_6v	pro - bas	-0.0026740	-0.1744796	0.8620570
R_6v	proactive	0.0407046	4.3851102	0.0000229
R_7PL	baseline	0.0322550	2.3269006	0.0116261
R_7PL	pro - bas	0.0095324	0.4979737	0.6202644
R_7PL	proactive	0.0417874	3.2167599	0.0010309
R_7Pm	baseline	0.0261672	2.3077895	0.0121808
R_7Pm	pro - bas	0.0010661	0.0656804	0.9478436
R_7Pm	proactive	0.0272333	2.7189178	0.0042419
R_8BL	baseline	0.0303858	3.6253940	0.0002920
R_8BL	pro - bas	-0.0034594	-0.2748774	0.7843244
R_8BL	proactive	0.0269264	2.9122600	0.0024909
R_8BM	baseline	0.0333144	3.2335994	0.0009804
R_8BM	pro - bas	0.0108933	0.8639401	0.3909509
R_8BM	proactive	0.0442077	4.8084055	0.0000050
R_a32pr	baseline	0.0270071	2.3653583	0.0105774
R_a32pr	pro - bas	0.0065047	0.4342448	0.6656182
R_a32pr	proactive	0.0335118	3.2264370	0.0010016
R_d23ab	baseline	0.0462789	3.0778101	0.0015519
R_d23ab	pro - bas	0.0014143	0.0717626	0.9430218
R_d23ab	proactive	0.0476932	3.5286503	0.0003966
R_d32	baseline	0.0320152	3.2011853	0.0010798
R_d32	pro - bas	-0.0137954	-1.1525903	0.2535032
R_d32	proactive	0.0182198	2.3209984	0.0117950
R_FEF	baseline	0.0229212	2.6306115	0.0053688
R_FEF	pro - bas	0.0093026	0.7110095	0.4797447
R_FEF	proactive	0.0322238	3.5785726	0.0003388
R_FOP1	baseline	0.0453015	4.1311364	0.0000550
R_FOP1	pro - bas	-0.0051314	-0.3174846	0.7519426
R_FOP1	proactive	0.0401702	3.1945272	0.0011014
R_FOP4	baseline	0.0320719	2.6896106	0.0045893
R_FOP4	pro - bas	0.0029333	0.2019794	0.8405941
R_FOP4	proactive	0.0350052	3.0885870	0.0015040
R_IFJa	baseline	0.0581607	4.1261433	0.0000559
R_IFJa	pro - bas	-0.0282809	-1.8821788	0.0645069
R_IFJa	proactive	0.0298797	2.8041546	0.0033638
R_IFJp	baseline	0.0421848	2.7223415	0.0042029
R_IFJp	pro - bas	-0.0157123	-0.8312603	0.4090166
R_IFJp	proactive	0.0264726	2.4643302	0.0082564
R_IFSa	baseline	0.0228276	2.5137140	0.0072791
R_IFSa	pro - bas	0.0019892	0.1597799	0.8735739
R_IFSa	proactive	0.0248168	2.8961656	0.0026060
R_IFSp	baseline	0.0360302	3.4389260	0.0005247
R_IFSp	pro - bas	-0.0110350	-0.7677872	0.4455280
R_IFSp	proactive	0.0249952	2.5805372	0.0061230
R_IP0	baseline	0.0581368	4.8214525	0.0000048
R_IP0	pro - bas	0.0027214	0.1633891	0.8707435
R_IP0	proactive	0.0608582	4.7631274	0.0000059
R_IP1	baseline	0.0620895	5.5476399	0.0000003
R_IP1	pro - bas	-0.0073704	-0.4623896	0.6454206
R_IP1	proactive	0.0547190	4.3960019	0.0000220
R_LIPd	baseline	0.0543875	5.1997642	0.0000012
R_LIPd	pro - bas	-0.0197143	-1.1390711	0.2590555
R_LIPd	proactive	0.0346732	2.5757677	0.0061997
R_MIP	baseline	0.0400735	3.4293939	0.0005404
R_MIP	pro - bas	-0.0147576	-1.0278038	0.3080354
R_MIP	proactive	0.0253159	2.7221350	0.0042052
R_p32pr	baseline	0.0320380	3.1308331	0.0013293
R_p32pr	pro - bas	-0.0088120	-0.6211953	0.5367484
R_p32pr	proactive	0.0232260	2.5360839	0.0068718
R_p9-46v	baseline	0.0217215	2.2837716	0.0129113
R_p9-46v	pro - bas	0.0108279	0.9806992	0.3305525
R_p9-46v	proactive	0.0325494	3.9455501	0.0001028
R_PFm	baseline	0.0239998	3.3152090	0.0007667
R_PFm	pro - bas	0.0103038	1.0038273	0.3193637
R_PFm	proactive	0.0343036	3.9354076	0.0001063
R_PGi	baseline	0.0515191	5.1995900	0.0000012
R_PGi	pro - bas	-0.0227865	-1.7961116	0.0773492
R_PGi	proactive	0.0287327	3.1955388	0.0010981
R_PGp	baseline	0.0428454	4.3297395	0.0000277
R_PGp	pro - bas	-0.0193038	-1.4175827	0.1613189
R_PGp	proactive	0.0235416	2.3028962	0.0123266
R_PGs	baseline	0.0346951	3.5298787	0.0003951
R_PGs	pro - bas	-0.0117933	-0.8651640	0.3902841
R_PGs	proactive	0.0229018	2.1958271	0.0159274
R_PH	baseline	0.0503501	4.3746103	0.0000237
R_PH	pro - bas	-0.0257769	-1.7917309	0.0780563
R_PH	proactive	0.0245732	2.4366043	0.0088554
R_POS2	baseline	0.0451087	3.8731729	0.0001307
R_POS2	pro - bas	-0.0228003	-1.6413463	0.1057892
R_POS2	proactive	0.0223084	2.5381043	0.0068360
R_ProS	baseline	0.0350273	3.1308786	0.0013291
R_ProS	pro - bas	-0.0056638	-0.2933605	0.7702263
R_ProS	proactive	0.0293635	2.2187903	0.0150854
R_RI	baseline	0.0238294	2.2793175	0.0130509
R_RI	pro - bas	-0.0005509	-0.0374870	0.9702172
R_RI	proactive	0.0232785	2.3069240	0.0122065
R_s6-8	baseline	0.0380854	2.8338935	0.0030991
R_s6-8	pro - bas	0.0059065	0.3668575	0.7149744
R_s6-8	proactive	0.0439919	3.5134776	0.0004160
R_SCEF	baseline	0.0321544	3.1924698	0.0011082
R_SCEF	pro - bas	-0.0004331	-0.0362714	0.9711825
R_SCEF	proactive	0.0317213	3.3343443	0.0007234
R_STSda	baseline	0.0271693	3.0596006	0.0016360
R_STSda	pro - bas	-0.0034708	-0.2890468	0.7735099
R_STSda	proactive	0.0236985	2.5941518	0.0059090
R_TPOJ1	baseline	0.0176715	1.9388050	0.0285401
R_TPOJ1	pro - bas	0.0103060	0.8031345	0.4249653
R_TPOJ1	proactive	0.0279775	3.1695947	0.0011859
R_TPOJ2	baseline	0.0223106	2.2621555	0.0136015
R_TPOJ2	pro - bas	0.0083759	0.6435096	0.5222667
R_TPOJ2	proactive	0.0306865	3.9801317	0.0000916
R_V2	baseline	0.0321044	3.9796844	0.0000917
R_V2	pro - bas	-0.0096601	-0.8160976	0.4175688
R_V2	proactive	0.0224443	2.9982247	0.0019521
R_v23ab	baseline	0.0424358	3.7800550	0.0001774
R_v23ab	pro - bas	-0.0136777	-0.7967892	0.4286142
R_v23ab	proactive	0.0287581	2.1313907	0.0185142
R_V3	baseline	0.0402717	4.3378285	0.0000270
R_V3	pro - bas	-0.0118450	-0.9340413	0.3539062
R_V3	proactive	0.0284267	3.4485041	0.0005094
R_V3CD	baseline	0.0606120	4.3821135	0.0000231
R_V3CD	pro - bas	-0.0344533	-1.8620824	0.0673321
R_V3CD	proactive	0.0261587	2.1993893	0.0157941
R_V4	baseline	0.0297084	2.9977525	0.0019547
R_V4	pro - bas	-0.0072166	-0.5800767	0.5639652
R_V4	proactive	0.0224918	2.7031072	0.0044262

## `summarise()` has grouped output by 'term'. You can override using the `.groups` argument.

[[1]]

bias target

region	session	m	t_stat	p
L_4	baseline	0.0105178	4.4265750	0.0000198
L_4	pro - bas	-0.0050921	-1.8196064	0.0736476
L_4	proactive	0.0054256	4.0708312	0.0000675
L_IFJp	baseline	0.0141939	3.0546861	0.0016595
L_IFJp	pro - bas	-0.0046507	-0.9451944	0.3482287
L_IFJp	proactive	0.0095432	3.4586928	0.0004935
L_IP1	baseline	0.0133677	2.6030923	0.0057721
L_IP1	pro - bas	-0.0048805	-0.9738529	0.3339139
L_IP1	proactive	0.0084872	3.0006677	0.0019385
L_LIPd	baseline	0.0161102	3.6840644	0.0002420
L_LIPd	pro - bas	-0.0068371	-1.3793460	0.1727433
L_LIPd	proactive	0.0092732	3.2371084	0.0009701
L_RI	baseline	0.0099509	2.9156557	0.0024673
L_RI	pro - bas	-0.0037897	-0.9180987	0.3621251
L_RI	proactive	0.0061612	2.9726357	0.0020999
L_SCEF	baseline	0.0187824	5.0391057	0.0000022
L_SCEF	pro - bas	-0.0114097	-2.9444209	0.0045499
L_SCEF	proactive	0.0073728	4.7407686	0.0000064
L_STSdp	baseline	0.0113015	2.9568042	0.0021965
L_STSdp	pro - bas	-0.0038761	-0.8747923	0.3850632
L_STSdp	proactive	0.0074254	2.9689941	0.0021218
R_1	baseline	0.0079756	2.8399664	0.0030475
R_1	pro - bas	0.0001187	0.0353203	0.9719378
R_1	proactive	0.0080943	4.2243711	0.0000400
R_3b	baseline	0.0103628	3.7531312	0.0001936
R_3b	pro - bas	-0.0018198	-0.5917747	0.5561533
R_3b	proactive	0.0085430	5.3553529	0.0000007
R_V3	baseline	0.0111607	4.0831468	0.0000647
R_V3	pro - bas	-0.0047249	-1.4602386	0.1492735
R_V3	proactive	0.0064358	4.1727920	0.0000477
R_V8	baseline	0.0142793	2.5763219	0.0061907
R_V8	pro - bas	-0.0001645	-0.0252239	0.9799574
R_V8	proactive	0.0141148	3.6538804	0.0002666

[[2]]

bias distractor

region	session	m	t_stat	p
L_FFC	baseline	0.0130309	2.7385069	0.0040233
L_FFC	pro - bas	-0.0067809	-1.2616702	0.2117936
L_FFC	proactive	0.0062500	3.1214043	0.0013665
L_V1	baseline	0.0187363	4.9167257	0.0000034
L_V1	pro - bas	-0.0027061	-0.7707996	0.4437532
L_V1	proactive	0.0160302	5.5484856	0.0000003
L_V2	baseline	0.0174852	4.3595060	0.0000250
L_V2	pro - bas	-0.0007692	-0.1789910	0.8585284
L_V2	proactive	0.0167161	5.9017659	0.0000001
L_V3	baseline	0.0247319	4.9458045	0.0000030
L_V3	pro - bas	-0.0030339	-0.5672934	0.5725632
L_V3	proactive	0.0216981	5.5267734	0.0000003
L_V4	baseline	0.0117444	3.0059468	0.0019094
L_V4	pro - bas	-0.0008110	-0.1910941	0.8490763
L_V4	proactive	0.0109334	4.5474591	0.0000129
R_V1	baseline	0.0141985	3.9072196	0.0001168
R_V1	pro - bas	-0.0024333	-0.6304332	0.5307281
R_V1	proactive	0.0117652	5.2978488	0.0000008
R_V2	baseline	0.0164023	5.4195061	0.0000005
R_V2	pro - bas	-0.0021291	-0.6132187	0.5419749
R_V2	proactive	0.0142732	6.2546568	0.0000000
R_V3	baseline	0.0264743	6.3471662	0.0000000
R_V3	pro - bas	-0.0107110	-2.7945169	0.0069079
R_V3	proactive	0.0157633	6.3680933	0.0000000
R_V4	baseline	0.0193733	4.8183405	0.0000049
R_V4	pro - bas	-0.0092954	-2.2422150	0.0285341
R_V4	proactive	0.0100780	4.8426600	0.0000044

[[3]]

bias incongruency

region	session	m	t_stat	p
L_2	baseline	0.0440436	6.3732008	0.0000000
L_2	pro - bas	-0.0305906	-3.4703800	0.0009518
L_2	proactive	0.0134530	2.5567555	0.0065139
L_44	baseline	0.0627007	7.0598226	0.0000000
L_44	pro - bas	-0.0297864	-2.8607772	0.0057531
L_44	proactive	0.0329143	4.2487652	0.0000367
L_6a	baseline	0.0776408	8.4562962	0.0000000
L_6a	pro - bas	-0.0550228	-5.5732311	0.0000006
L_6a	proactive	0.0226179	3.3316391	0.0007294
L_6ma	baseline	0.0652127	5.8885810	0.0000001
L_6ma	pro - bas	-0.0418598	-3.2501915	0.0018657
L_6ma	proactive	0.0233528	2.6080788	0.0056971
L_6r	baseline	0.0822818	8.3356220	0.0000000
L_6r	pro - bas	-0.0548245	-5.0351371	0.0000044
L_6r	proactive	0.0274573	3.8635094	0.0001349
L_6v	baseline	0.0709255	6.7132234	0.0000000
L_6v	pro - bas	-0.0362703	-3.1768357	0.0023215
L_6v	proactive	0.0346552	4.2020626	0.0000432
L_7Am	baseline	0.0333142	3.0042699	0.0019186
L_7Am	pro - bas	-0.0124902	-0.9659339	0.3378300
L_7Am	proactive	0.0208240	3.4701379	0.0004763
L_8BM	baseline	0.0782112	8.3394970	0.0000000
L_8BM	pro - bas	-0.0458588	-3.9754078	0.0001861
L_8BM	proactive	0.0323524	4.8491358	0.0000043
L_8C	baseline	0.1298254	12.3899507	0.0000000
L_8C	pro - bas	-0.1043779	-8.3166818	0.0000000
L_8C	proactive	0.0254474	3.8949796	0.0001216
L_a24pr	baseline	0.0313433	3.4987423	0.0004356
L_a24pr	pro - bas	-0.0058798	-0.4622512	0.6455193
L_a24pr	proactive	0.0254635	2.7180182	0.0042522
L_AIP	baseline	0.1188968	10.4426462	0.0000000
L_AIP	pro - bas	-0.0851012	-6.9132468	0.0000000
L_AIP	proactive	0.0337956	4.0800821	0.0000654
L_FEF	baseline	0.0685446	8.1419616	0.0000000
L_FEF	pro - bas	-0.0374198	-3.5607511	0.0007170
L_FEF	proactive	0.0311248	3.7460637	0.0001981
L_FOP4	baseline	0.0946682	8.6944171	0.0000000
L_FOP4	pro - bas	-0.0771766	-6.9153588	0.0000000
L_FOP4	proactive	0.0174916	2.7649000	0.0037450
L_FOP5	baseline	0.0756334	5.9784118	0.0000001
L_FOP5	pro - bas	-0.0450335	-3.3219886	0.0015022
L_FOP5	proactive	0.0305998	4.4714420	0.0000169
L_i6-8	baseline	0.1131245	8.8652137	0.0000000
L_i6-8	pro - bas	-0.0813786	-5.6286842	0.0000005
L_i6-8	proactive	0.0317458	3.4697455	0.0004768
L_IFJa	baseline	0.1037815	9.0227344	0.0000000
L_IFJa	pro - bas	-0.0804934	-5.3371120	0.0000014
L_IFJa	proactive	0.0232881	2.6924533	0.0045545
L_IFJp	baseline	0.1035532	7.4976211	0.0000000
L_IFJp	pro - bas	-0.0602202	-3.5513929	0.0007384
L_IFJp	proactive	0.0433329	4.3601015	0.0000250
L_IFSa	baseline	0.1044481	9.3324104	0.0000000
L_IFSa	pro - bas	-0.0585230	-4.4385288	0.0000379
L_IFSa	proactive	0.0459252	4.2311259	0.0000390
L_IFSp	baseline	0.1128993	8.2850119	0.0000000
L_IFSp	pro - bas	-0.0821299	-5.2301313	0.0000021
L_IFSp	proactive	0.0307695	3.2771138	0.0008603
L_IP0	baseline	0.1144241	8.8548976	0.0000000
L_IP0	pro - bas	-0.0844680	-5.9087413	0.0000002
L_IP0	proactive	0.0299561	3.3917379	0.0006069
L_IP1	baseline	0.1605688	12.0679848	0.0000000
L_IP1	pro - bas	-0.1002203	-7.0261082	0.0000000
L_IP1	proactive	0.0603485	5.4808039	0.0000004
L_IP2	baseline	0.1274545	9.7923068	0.0000000
L_IP2	pro - bas	-0.0893015	-5.6416816	0.0000004
L_IP2	proactive	0.0381529	3.8943427	0.0001218
L_IPS1	baseline	0.1009390	8.2226012	0.0000000
L_IPS1	pro - bas	-0.0700892	-5.2939201	0.0000017
L_IPS1	proactive	0.0308498	3.7049640	0.0002262
L_LIPd	baseline	0.1374634	10.1247619	0.0000000
L_LIPd	pro - bas	-0.0827873	-5.8020643	0.0000002
L_LIPd	proactive	0.0546761	4.7528706	0.0000062
L_MIP	baseline	0.0908872	6.8492150	0.0000000
L_MIP	pro - bas	-0.0610221	-4.1356028	0.0001083
L_MIP	proactive	0.0298652	3.1571654	0.0012302
L_p32pr	baseline	0.0671926	8.2854837	0.0000000
L_p32pr	pro - bas	-0.0433164	-4.5606336	0.0000246
L_p32pr	proactive	0.0238762	3.1695565	0.0011860
L_p9-46v	baseline	0.1322322	10.0914128	0.0000000
L_p9-46v	pro - bas	-0.1066311	-8.1073066	0.0000000
L_p9-46v	proactive	0.0256011	3.1018548	0.0014469
L_PEF	baseline	0.1035129	7.3200266	0.0000000
L_PEF	pro - bas	-0.0623220	-4.2525318	0.0000725
L_PEF	proactive	0.0411910	4.1208549	0.0000570
L_PFcm	baseline	0.0765672	6.8889378	0.0000000
L_PFcm	pro - bas	-0.0323660	-2.1893929	0.0323412
L_PFcm	proactive	0.0442012	3.7409945	0.0002014
L_PFop	baseline	0.0670375	5.8537530	0.0000001
L_PFop	pro - bas	-0.0454523	-3.2135967	0.0020814
L_PFop	proactive	0.0215852	2.5224304	0.0071179
L_PFt	baseline	0.0756435	6.5347537	0.0000000
L_PFt	pro - bas	-0.0478629	-4.4424054	0.0000374
L_PFt	proactive	0.0277806	3.6038501	0.0003127
L_SCEF	baseline	0.0865741	11.0773376	0.0000000
L_SCEF	pro - bas	-0.0483153	-4.8081666	0.0000101
L_SCEF	proactive	0.0382588	4.9018576	0.0000036
L_SFL	baseline	0.0741188	7.5936974	0.0000000
L_SFL	pro - bas	-0.0424488	-3.9057987	0.0002346
L_SFL	proactive	0.0316701	4.6413327	0.0000092
L_STV	baseline	0.0670425	7.0861017	0.0000000
L_STV	pro - bas	-0.0481076	-4.8721379	0.0000080
L_STV	proactive	0.0189350	2.8332554	0.0031046
L_TPOJ1	baseline	0.0657798	5.0801181	0.0000019
L_TPOJ1	pro - bas	-0.0441469	-3.3141322	0.0015384
L_TPOJ1	proactive	0.0216329	2.8590668	0.0028902
L_VIP	baseline	0.0398614	3.3948238	0.0006012
L_VIP	pro - bas	-0.0150741	-1.2892347	0.2021057
L_VIP	proactive	0.0247873	3.1291338	0.0013359
R_1	baseline	0.0200742	3.5560650	0.0003638
R_1	pro - bas	-0.0060326	-0.7516098	0.4551294
R_1	proactive	0.0140416	2.5388565	0.0068228
R_44	baseline	0.0542405	4.7981012	0.0000052
R_44	pro - bas	-0.0294597	-2.1933836	0.0320387
R_44	proactive	0.0247808	3.7808990	0.0001769
R_6a	baseline	0.0531351	6.9274053	0.0000000
R_6a	pro - bas	-0.0331047	-3.2455318	0.0018919
R_6a	proactive	0.0200304	3.6031985	0.0003134
R_6ma	baseline	0.0665931	7.7047138	0.0000000
R_6ma	pro - bas	-0.0356329	-3.1633420	0.0024159
R_6ma	proactive	0.0309602	4.5731446	0.0000118
R_8BM	baseline	0.0799758	7.5284278	0.0000000
R_8BM	pro - bas	-0.0546368	-4.7008176	0.0000149
R_8BM	proactive	0.0253390	3.4983964	0.0004361
R_A5	baseline	0.0289726	4.1204854	0.0000570
R_A5	pro - bas	-0.0118641	-1.2928625	0.2008557
R_A5	proactive	0.0171085	2.9239308	0.0024105
R_FEF	baseline	0.0541432	5.9474122	0.0000001
R_FEF	pro - bas	-0.0355550	-3.0887013	0.0030070
R_FEF	proactive	0.0185882	2.5128892	0.0072945
R_FOP5	baseline	0.0751667	6.8105134	0.0000000
R_FOP5	pro - bas	-0.0527226	-4.3004538	0.0000614
R_FOP5	proactive	0.0224441	2.6406497	0.0052282
R_IFJa	baseline	0.0707536	5.2205589	0.0000011
R_IFJa	pro - bas	-0.0467337	-2.8471787	0.0059744
R_IFJa	proactive	0.0240199	2.5674762	0.0063350
R_IFJp	baseline	0.0758448	5.8816558	0.0000001
R_IFJp	pro - bas	-0.0435221	-3.2813288	0.0016989
R_IFJp	proactive	0.0323227	3.5834295	0.0003337
R_IFSp	baseline	0.0889198	7.9356230	0.0000000
R_IFSp	pro - bas	-0.0594514	-4.8978170	0.0000073
R_IFSp	proactive	0.0294684	3.9636692	0.0000968
R_IP2	baseline	0.0789511	6.7677039	0.0000000
R_IP2	pro - bas	-0.0574684	-4.0694410	0.0001356
R_IP2	proactive	0.0214827	2.7131062	0.0043088
R_IPS1	baseline	0.1029098	7.5110154	0.0000000
R_IPS1	pro - bas	-0.0795760	-5.1250720	0.0000031
R_IPS1	proactive	0.0233338	2.6787166	0.0047250
R_LIPd	baseline	0.0766589	6.5330034	0.0000000
R_LIPd	pro - bas	-0.0496233	-3.4290108	0.0010821
R_LIPd	proactive	0.0270356	2.9553860	0.0022054
R_MIP	baseline	0.0908465	7.8527802	0.0000000
R_MIP	pro - bas	-0.0639325	-5.1486826	0.0000029
R_MIP	proactive	0.0269140	3.5587276	0.0003608
R_PEF	baseline	0.0824335	6.7068928	0.0000000
R_PEF	pro - bas	-0.0508498	-3.5502529	0.0007411
R_PEF	proactive	0.0315838	3.3507349	0.0006882
R_PFt	baseline	0.0342334	3.5063255	0.0004254
R_PFt	pro - bas	-0.0116017	-0.9734372	0.3341187
R_PFt	proactive	0.0226317	2.6217711	0.0054955
R_SCEF	baseline	0.0524407	5.7620206	0.0000001
R_SCEF	pro - bas	-0.0258615	-2.1145038	0.0385001
R_SCEF	proactive	0.0265791	3.2898747	0.0008278

3.2 Proactive - Baseline difference analysis

Relative to baseline session, 10% of regions with largest absolute change in proactive. Separately for bias and pc50 items and each model. Negative sign indicates baseline session was stronger.

for (ttypesub in ttype_subsets) {
  
  d <- enlist(c("target", "distractor", "incongruency"))
  for (model in names(d)) {
    
    d[[model]] <- dat_sum %>% 
        filter(session == "pro - bas", ttype_subset == ttypesub, term == model) %>%
        mutate(abs_t = abs(t_stat)) %>%
        slice_max(abs_t, prop = 0.1) %>% 
        ungroup %>%
        select(region, m, t_stat, p) %>%
        arrange(t_stat)
      
  }
  
  print(lapply(names(d), function(x) kable(d[[x]], caption = paste0(ttypesub, " ", x))))
  
}

[[1]]

pc50 target

region	m	t_stat	p
R_9m	-0.0177344	-3.139649	0.0025906
L_13l	-0.0230708	-3.116665	0.0027713
R_31pd	-0.0293633	-2.812791	0.0065697
R_PHA3	-0.0230417	-2.636775	0.0105642
R_STV	-0.0184597	-2.507935	0.0147756
R_47l	-0.0211835	-2.491830	0.0153970
R_5m	-0.0204645	-2.290647	0.0253966
L_PGi	-0.0183358	-2.281131	0.0259878
L_PBelt	-0.0174405	-2.278013	0.0261841
L_8C	-0.0161685	-2.251861	0.0278832
L_PEF	-0.0194609	-2.246034	0.0282748
L_FFC	-0.0133591	-2.108237	0.0390590
L_5mv	-0.0135988	-2.075106	0.0421328
L_d32	-0.0158456	-2.055598	0.0440389
L_V6A	-0.0213429	-2.033091	0.0463304
R_5mv	-0.0132839	-1.950596	0.0556290
R_6mp	-0.0112439	-1.939127	0.0570401
R_OP4	-0.0129327	-1.931742	0.0579649
R_LIPd	-0.0192152	-1.913118	0.0603537
R_TPOJ3	-0.0168858	-1.878406	0.0650295
L_IFSa	-0.0169793	-1.873336	0.0657375
R_i6-8	-0.0177443	-1.856528	0.0681311
L_11l	-0.0151238	-1.851810	0.0688161
L_STSda	-0.0156874	-1.832186	0.0717274
L_STSdp	-0.0109282	-1.818581	0.0738060
R_IFSa	-0.0140478	-1.800914	0.0765801
L_LO3	-0.0199444	-1.784926	0.0791653
R_LO1	-0.0163107	-1.783017	0.0794787
R_FOP1	-0.0182600	-1.781797	0.0796797
L_8Av	-0.0141846	-1.763928	0.0826705
L_MT	0.0205927	1.863164	0.0671775
L_PoI1	0.0160030	1.875809	0.0653914
R_FOP4	0.0160675	1.962206	0.0542308
L_RI	0.0138888	2.101798	0.0396406
L_PH	0.0158492	2.515669	0.0144853

[[2]]

pc50 distractor

region	m	t_stat	p
R_STV	-0.0224215	-3.501822	0.0008628
R_PreS	-0.0275640	-2.922989	0.0048337
L_IFJp	-0.0157307	-2.403332	0.0192512
L_V8	-0.0206731	-2.355283	0.0216874
R_IFJp	-0.0201048	-2.251110	0.0279334
L_10d	-0.0165336	-2.232363	0.0292126
R_8BL	-0.0176080	-2.163537	0.0343623
R_IFSa	-0.0159465	-2.131417	0.0370262
R_TA2	-0.0160526	-2.129899	0.0371565
L_TPOJ3	-0.0196291	-2.076591	0.0419906
R_STSva	-0.0148773	-2.028190	0.0468428
L_PHA3	-0.0152003	-2.001255	0.0497468
R_V1	-0.0090104	-1.995736	0.0503606
R_25	-0.0196192	-1.920259	0.0594281
R_TE1a	-0.0111865	-1.846049	0.0696602
L_s6-8	-0.0210867	-1.775426	0.0807355
R_a32pr	-0.0144108	-1.771386	0.0814110
R_STSvp	-0.0147477	-1.770625	0.0815388
R_FOP2	-0.0258548	-1.765611	0.0823849
L_7m	-0.0168196	-1.704061	0.0933771
R_FOP5	-0.0126242	-1.689260	0.0961930
R_8C	-0.0104230	-1.682074	0.0975850
L_TE2a	-0.0080001	-1.663334	0.1012929
R_TE2p	-0.0094968	-1.629928	0.1081873
R_6r	0.0115989	1.591153	0.1166615
R_TPOJ2	0.0129277	1.697062	0.0947001
R_d23ab	0.0234043	1.749102	0.0852223
L_6ma	0.0099653	1.775329	0.0807517
R_TPOJ3	0.0168381	1.820733	0.0734739
R_10v	0.0108303	1.861672	0.0673909
R_FEF	0.0157345	2.124398	0.0376318
R_A1	0.0197549	2.280563	0.0260235
L_3b	0.0091341	2.281313	0.0259764
R_MBelt	0.0188773	2.700499	0.0089146
R_SCEF	0.0181595	2.860374	0.0057595

[[3]]

pc50 incongruency

region	m	t_stat	p
L_PGi	-0.0461496	-3.489941	0.0008955
L_DVT	-0.0507079	-3.298267	0.0016142
L_TPOJ2	-0.0474032	-3.289948	0.0016553
L_AIP	-0.0433342	-3.177417	0.0023175
L_FOP4	-0.0341188	-2.723754	0.0083738
L_7PC	-0.0320530	-2.703978	0.0088317
L_STSvp	-0.0296271	-2.685773	0.0092734
L_IFSp	-0.0461277	-2.483903	0.0157115
L_STSdp	-0.0332362	-2.479402	0.0158926
R_AIP	-0.0355677	-2.451271	0.0170677
L_IFJa	-0.0430783	-2.411665	0.0188546
R_IPS1	-0.0326018	-2.401844	0.0193228
L_pOFC	-0.0291833	-2.298078	0.0249433
R_i6-8	-0.0324402	-2.271938	0.0265704
R_TF	-0.0166461	-2.249656	0.0280308
R_2	-0.0205738	-2.202057	0.0313899
L_LIPd	-0.0365356	-2.201070	0.0314632
R_STV	-0.0243787	-2.179654	0.0330899
R_VIP	-0.0294313	-2.173995	0.0335318
L_PEF	-0.0369481	-2.141929	0.0361352
L_9m	-0.0212864	-2.090373	0.0406912
R_a24	-0.0320197	-2.047311	0.0448710
L_d23ab	-0.0331344	-1.974927	0.0527336
L_STSva	-0.0174164	-1.922191	0.0591797
R_V8	-0.0316690	-1.909768	0.0607921
L_LIPv	-0.0286405	-1.904407	0.0614994
R_IFJa	-0.0282809	-1.882179	0.0645069
R_V3CD	-0.0344533	-1.862082	0.0673321
R_9m	-0.0179402	-1.822300	0.0732329
R_8Av	-0.0246114	-1.820633	0.0734893
R_PGi	-0.0227865	-1.796112	0.0773492
R_PH	-0.0257769	-1.791731	0.0780563
R_PF	-0.0265371	-1.788627	0.0785605
L_V8	-0.0352135	-1.784983	0.0791559
L_TA2	0.0392024	2.623581	0.0109387

[[1]]

bias target

region	m	t_stat	p
L_IFSp	-0.0268525	-5.072759	0.0000038
L_8C	-0.0180133	-4.853131	0.0000086
R_6a	-0.0137063	-4.608107	0.0000208
L_ProS	-0.0205743	-3.811706	0.0003199
L_FOP5	-0.0165426	-3.182449	0.0022832
L_PHA1	-0.0193576	-3.125410	0.0027012
L_PFm	-0.0140197	-3.109295	0.0028317
L_IP2	-0.0191342	-3.107381	0.0028475
R_IP1	-0.0163877	-2.992851	0.0039646
R_V3A	-0.0163378	-2.966849	0.0042695
L_SCEF	-0.0114097	-2.944421	0.0045499
L_46	-0.0118663	-2.742847	0.0079525
L_V3	-0.0097893	-2.700141	0.0089232
R_IFSp	-0.0121804	-2.679295	0.0094353
R_RI	-0.0152330	-2.675069	0.0095424
L_3b	-0.0087499	-2.662799	0.0098595
L_3a	-0.0085067	-2.604325	0.0115070
R_V1	-0.0087678	-2.570069	0.0125847
R_AIP	-0.0125738	-2.565659	0.0127299
R_46	-0.0131433	-2.543272	0.0134907
R_SFL	-0.0118203	-2.522875	0.0142194
L_6a	-0.0098836	-2.515445	0.0144936
L_p32pr	-0.0112593	-2.483936	0.0157102
L_FOP4	-0.0119043	-2.474843	0.0160779
L_IFSa	-0.0122984	-2.420395	0.0184469
R_52	-0.0181887	-2.396676	0.0195734
L_9-46d	-0.0098769	-2.395763	0.0196179
L_PGi	-0.0120188	-2.330793	0.0230318
L_AIP	-0.0113568	-2.304410	0.0245627
L_FEF	-0.0107215	-2.286783	0.0256352
R_33pr	-0.0137511	-2.278372	0.0261615
L_SFL	-0.0099751	-2.257008	0.0275413
L_PFcm	-0.0136509	-2.195025	0.0319151
R_V2	-0.0063752	-2.191619	0.0321722
L_MBelt	0.0115537	2.511568	0.0146386

[[2]]

bias distractor

region	m	t_stat	p
L_AIP	-0.0212877	-4.674952	0.0000163
R_IP1	-0.0184820	-3.493357	0.0008860
R_AIP	-0.0153805	-3.469555	0.0009542
L_23c	-0.0139228	-3.260545	0.0018086
L_3a	-0.0100848	-3.193939	0.0022067
L_31a	-0.0213344	-3.126364	0.0026936
R_6d	-0.0137850	-3.073493	0.0031429
L_ProS	-0.0197267	-3.048726	0.0033766
L_8C	-0.0132799	-3.011497	0.0037585
L_45	-0.0134355	-3.008660	0.0037892
L_43	-0.0131775	-3.004851	0.0038308
R_46	-0.0134912	-2.951793	0.0044559
L_IFJp	-0.0127375	-2.943459	0.0045623
R_PIT	-0.0188870	-2.938775	0.0046231
L_IFSa	-0.0143782	-2.937558	0.0046391
L_3b	-0.0101955	-2.925416	0.0048008
R_V3	-0.0107110	-2.794517	0.0069079
L_10r	-0.0171208	-2.721465	0.0084257
L_2	-0.0098227	-2.679113	0.0094399
L_1	-0.0087764	-2.626309	0.0108603
L_SCEF	-0.0098496	-2.597290	0.0117212
R_RI	-0.0131179	-2.488106	0.0155440
L_IP1	-0.0130168	-2.473957	0.0161142
L_p47r	-0.0136382	-2.447032	0.0172514
L_RI	-0.0123859	-2.416361	0.0186343
L_AAIC	-0.0197768	-2.385860	0.0201072
L_PHT	-0.0114865	-2.341141	0.0224549
L_TPOJ1	-0.0115369	-2.284238	0.0257935
R_V4	-0.0092954	-2.242215	0.0285341
R_LIPd	-0.0126245	-2.202577	0.0313514
L_OP1	-0.0126697	-2.199893	0.0315507
L_TF	-0.0059109	-2.197012	0.0317659
L_IP2	-0.0097816	-2.195516	0.0318781
L_d32	0.0126380	2.207770	0.0309689
R_FOP2	0.0164284	2.360591	0.0214053

[[3]]

bias incongruency

region	m	t_stat	p
L_8C	-0.1043779	-8.316682	0.0e+00
L_p9-46v	-0.1066311	-8.107307	0.0e+00
R_IP1	-0.0958843	-7.523548	0.0e+00
L_PF	-0.0834004	-7.214125	0.0e+00
R_IP0	-0.1047275	-7.150769	0.0e+00
L_IP1	-0.1002203	-7.026108	0.0e+00
L_7m	-0.0871959	-6.942213	0.0e+00
L_FOP4	-0.0771766	-6.915359	0.0e+00
L_AIP	-0.0851012	-6.913247	0.0e+00
R_POS2	-0.0775834	-6.380421	0.0e+00
R_AIP	-0.0766218	-6.361702	0.0e+00
L_8Av	-0.0599460	-6.331417	0.0e+00
L_V3CD	-0.0797816	-6.119921	1.0e-07
L_POS2	-0.0737083	-6.078117	1.0e-07
L_PGp	-0.0875286	-6.062959	1.0e-07
R_PH	-0.0645990	-6.016553	1.0e-07
L_45	-0.0634733	-6.002445	1.0e-07
R_6r	-0.0664633	-5.996154	1.0e-07
L_IP0	-0.0844680	-5.908741	2.0e-07
L_LIPd	-0.0827873	-5.802064	2.0e-07
L_PFm	-0.0632621	-5.781269	3.0e-07
L_PHT	-0.0633172	-5.770060	3.0e-07
L_IP2	-0.0893015	-5.641682	4.0e-07
L_i6-8	-0.0813786	-5.628684	5.0e-07
L_6a	-0.0550228	-5.573231	6.0e-07
R_DVT	-0.0659682	-5.468045	9.0e-07
L_PGs	-0.0746622	-5.388997	1.2e-06
R_PGp	-0.0782775	-5.341079	1.4e-06
L_IFJa	-0.0804934	-5.337112	1.4e-06
L_8BL	-0.0600722	-5.322898	1.5e-06
R_8Av	-0.0502591	-5.315056	1.5e-06
L_IPS1	-0.0700892	-5.293920	1.7e-06
R_PFm	-0.0547894	-5.290968	1.7e-06
R_V3CD	-0.0768837	-5.246912	2.0e-06
L_IFSp	-0.0821299	-5.230131	2.1e-06

4 Bar plots

## prep for plotting:

dat_barplots <- dat %>% 
  mutate(
    term = factor(term, levels = c("target", "incongruency", "distractor")),
    region_nice = gsub("17Networks_", "", region)
    ) %>%
  filter(term %in% c("target", "distractor", "incongruency"))

dat_barplots <- dat_barplots %>%
  pivot_wider(names_from = "session", values_from = "b") %>%
  mutate(b = proactive - baseline, session = "pro - bas") %>%
  select(-proactive, -baseline) %>%
  rbind(dat_barplots)

4.1 ROIs

atlas$data %>%
  ggplot() +
    geom_brain(
      aes(fill = ifelse(region %in% rois$region, region, NA)),
      atlas = atlas, position = position_brain(side ~ hemi)
      ) +
  labs(title = paste0("ROIs: ", roiset), fill = NULL) +
  theme_surface

for (ttypesub in ttype_subsets) {
  
  p <- dat_barplots %>%
    
    filter(ttype_subset == ttypesub) %>%
    right_join(rois, by = "region") %>%
    
    ggplot(aes(region_nice, b, fill = term, color = term)) +
    geom_hline(yintercept = 0, color = "grey70") +
    stat_summary(
      fun.data = mean_cl_boot, geom = "errorbar", width = 0, size = 2/3, position = position_dodge(width = 0.5),
      color = "grey50"
      ) +
    stat_summary(fun = mean, geom = "col", width = 1/3, position = position_dodge(width = 0.5)) +
    
    scale_color_brewer(type = "qual", palette = 2) +
    scale_fill_brewer(type = "qual", palette = 2) +
    
    facet_grid(cols = vars(session), rows = vars(location), scales = "free_y") +
    theme(
      #axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5), 
      legend.position = "none", axis.title.y = element_blank()
      ) +
      labs(y = "mean coefficient", title = paste0("ROIs: ", roiset)) +
    coord_flip() +
    labs(title = ttypesub)
  
  print(p)
  
}